This invention relates to determination of contaminant material loading on a filter and more particularly to the use of radio frequencies to determine loading.
In many realms, there is a need for accurate sensing of the amount of material that has been captured by a filter. An example is the need to determine loading of air filters in HVAC systems, filter bag houses used in industrial applications, and even filters used with liquids, and the like.
Yet another example is the need to determine filter loading of soot on a particulate filter. The amount of loading on a particulate filter must be known in order to determine appropriate conditions for start-up of regeneration as well as monitoring conditions to determine when complete regeneration has been achieved. The level of loading is important in this context because regeneration of a particulate filter is often through an uncontrolled burn in which soot is ignited by the presence of free oxygen and a combustion wave is generated through the filter. Under certain conditions, it is possible that regeneration will produce temperatures that are very high, resulting in large thermal stresses that can result in limited fatigue life of the filter and ultimately, its destruction. Thus, the level of soot loading is important for successful filter regeneration.
A specific application of a particulate filter is a diesel particulate filter (DPF). Currently, filter pressure drop measurements are used to estimate the amount of soot accumulated in the DPF. In some cases, predictive models may also be used to estimate filter soot loading. Pressure drop measurements alone provide only an indirect and imprecise measure of soot accumulation in the particulate filter, and suffer from a number of disadvantages. Exhaust gas composition, temperature, and flow rates all affect filter pressure drop and must be accounted for to accurately relate pressure drop to filter soot loading. Further, the spatial distribution of the accumulated ash and soot also affects the pressure drop measurement, and this distribution may change with time, particularly as the filter becomes loaded with ash.
Following repeated regenerations, a substantial amount of ash may also accumulate in the particulate filter. Pressure drop measurements are unable to distinguish between soot and ash accumulation in the filter, the latter of which introduces additional error in soot load estimates based on pressure drop. Additionally, many types of filters exhibit a non-linear pressure drop response and pressure drop hysterisis depending upon the loading state and history of the filter, which further complicates pressure-based filter load measurements.
Pressure drop-based estimates of soot accumulation in the DPF are also characterized by generally slow response times and low sensitivities to small changes in soot load. Further, the inability of these systems to directly monitor ash levels in the filter requires the filters to be periodically inspected, resulting in vehicle or machine down-time, regardless of the actual filter ash level. Additionally, pressure-drop based measurements are unable to detect all but the most catastrophic of filter failures, and, in most cases, can not meet stringent on-board diagnostic requirements.
In order to address some of the shortcomings inherent to filter pressure drop measurements, various predictive models are generally used in conjunction with these measurements. While various types of models exist, many utilize a number of engine operating parameters, inputs from various engine and exhaust sensors, and the time between regeneration events, to predict the amount of soot accumulated in the DPF. In many cases, these models are uploaded in the engine control unit (ECU). These models are generally calibrated for a specific engine and fuel, requiring recalibration for each specific application. Furthermore, when used with cleaner burning fuels, (compared to the fuel with which the models were initially calibrated) the models tend to over-predict filter soot loading, resulting in unnecessary filter regenerations and fuel economy penalties. The combined use of predictive models and filter pressure drop measurements does little to overcome the deficiencies listed above. These shortcomings lead to inefficient system operation, fuel economy penalties, increased filter thermal cycling and fatigue, and reduced filter service life.
Exhaust gas soot sensors have also been proposed to measure the concentration of soot aerosols directly in the exhaust gas entering the particulate filter. These measurement systems suffer from the deficiency that the amount of soot accumulated on the particulate filter is not necessary equivalent to the amount of soot entering the filter, as some level of passive regeneration may take place, depending on exhaust conditions. Further, exhaust gas soot sensors provide no information on ash accumulation or soot and ash distribution in the DPF. In addition, many of these sensors suffer from soot fouling, consume excessive amounts of energy, and are subject to error introduced by exhaust temperature, exhaust gas velocity, and other factors.
Radio frequency (RF)-based particulate load monitoring systems have also been proposed. One such system monitors filter loading and initiates filter regeneration based on the magnitude of a low-frequency (RF) signal transmitted through the filter. This system, by restricting use to low frequencies below those required to establish resonance in a cavity, overlooks many of the advantages to utilizing higher frequencies required to generate multiple cavity resonant modes.
The use of microwaves to detect soot content in a particulate trap was also proposed. One such system detects soot content in a particulate filter by monitoring a change in filter resonant frequency. However, such systems cannot determine the spatial distribution of soot content within the particulate filter.
All RF and microwave filter load monitoring systems heretofore known suffer from a number of disadvantages:
(a) Prior Art systems are unable to monitor the spatial distribution of material accumulated in the filter. Ash accumulation in the filter displaces soot and alters its distribution. Further, non-uniform flow conditions may also result in non-uniform material accumulation.
(b) All known filter load monitoring systems initiate filter regeneration based on some average total filter soot load. Locally high soot loads cannot be detected by systems which are not capable of measuring material distribution in the filter.
(c) Previous microwave- and RF-based filter loading systems cannot simultaneously detect both soot and ash accumulation in the filter over all exhaust conditions.
(d) These systems do not detect filter failures or malfunctions, which is important to ensure the filter is operating as required.
(e) These systems do not detect malfunctions or failures of individual components, such as sensors, for example, that may be required for correct operation of the filter.
(f) Microwave and RF-based measurement systems are affected by moisture content and water vapor present in the exhaust gas and on the filter, which must be accounted for to reduce error in the measurement.
(g) Previous filter load monitoring systems fail to communicate with existing engine and exhaust sensors to provide feedback control capabilities useful to modify engine operation to optimize the combined engine and after-treatment system performance.
Therefore, it would be beneficial if there were a filter load measurement system that addressed the problems described above. Such a system would be advantageous in that lower emission limits may be achieved, while minimizing the amount of maintenance and unnecessary regeneration cycles. In addition, the use of multiple resonance modes would allow more detailed estimation of the special loading within the device.
In addition to particulate filters, the same filter load measurement system could be applied to a wide range of filters, including fiber filters, various porous media used for filtration, and the like. One example is air filters used in HVAC systems, where determination of the state of filter loading is important to determine when to clean or replace the filter. Similarly, bag-type filters are often cleaned by reverse flow and determining when to clean the filters based on the state of filter loading is also important.